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Data

DSPy is a machine learning framework, so working in it involves training sets, development sets, and test sets.

For each example in your data, we distinguish typically between three types of values: the inputs, the intermediate labels, and the final label. You can use DSPy effectively without any intermediate or final labels, but you will need at least a few example inputs.

How much data do I need and how do I collect data for my task?

Concretely, you can use DSPy optimizers usefully with as few as 10 example inputs, but having 50-100 examples (or even better, 300-500 examples) goes a long way.

How can you get examples like these? If your task is extremely unusual, please invest in preparing ~10 examples by hand. Often times, depending on your metric below, you just need inputs and not labels, so it's not that hard.

However, chances are that your task is not actually that unique. You can almost always find somewhat adjacent datasets on, say, HuggingFace datasets or other forms of data that you can leverage here.

If there's data whose licenses are permissive enough, we suggest you use them. Otherwise, you can also start using/deploying/demoing your system and collect some initial data that way.

DSPy Example objects

The core data type for data in DSPy is Example. You will use Examples to represent items in your training set and test set.

DSPy Examples are similar to Python dicts but have a few useful utilities. Your DSPy modules will return values of the type Prediction, which is a special sub-class of Example.

When you use DSPy, you will do a lot of evaluation and optimization runs. Your individual datapoints will be of type Example:

qa_pair = dspy.Example(question="This is a question?", answer="This is an answer.")

print(qa_pair)
print(qa_pair.question)
print(qa_pair.answer)
Output: 
Example({'question': 'This is a question?', 'answer': 'This is an answer.'}) (input_keys=None)
This is a question?
This is an answer.

Examples can have any field keys and any value types, though usually values are strings.

object = Example(field1=value1, field2=value2, field3=value3, ...)

You can now express your training set for example as:

trainset = [dspy.Example(report="LONG REPORT 1", summary="short summary 1"), ...]

Specifying Input Keys

In traditional ML, there are separated "inputs" and "labels".

In DSPy, the Example objects have a with_inputs() method, which can mark specific fields as inputs. (The rest are just metadata or labels.)

# Single Input.
print(qa_pair.with_inputs("question"))

# Multiple Inputs; be careful about marking your labels as inputs unless you mean it.
print(qa_pair.with_inputs("question", "answer"))

Values can be accessed using the .(dot) operator. You can access the value of key name in defined object Example(name="John Doe", job="sleep") through object.name.

To access or exclude certain keys, use inputs() and labels() methods to return new Example objects containing only input or non-input keys, respectively.

article_summary = dspy.Example(article= "This is an article.", summary= "This is a summary.").with_inputs("article")

input_key_only = article_summary.inputs()
non_input_key_only = article_summary.labels()

print("Example object with Input fields only:", input_key_only)
print("Example object with Non-Input fields only:", non_input_key_only)

Output 

Example object with Input fields only: Example({'article': 'This is an article.'}) (input_keys=None)
Example object with Non-Input fields only: Example({'summary': 'This is a summary.'}) (input_keys=None)

Loading Dataset from sources

One of the most convinient way to import dataset in DSPy is by using DataLoader. The first step is to declare an object, this object can then be used to call utilities to load datasets in different formats:

from dspy.datasets import DataLoader

dl = DataLoader()

For most dataset formats, it's quite straightforward you pass the file path to the corresponding method of the format and you'll get the list of Example for the dataset in return:

import pandas as pd

csv_dataset = dl.from_csv(
    "sample_dataset.csv",
    fields=("instruction", "context", "response"),
    input_keys=("instruction", "context")
)

json_dataset = dl.from_json(
    "sample_dataset.json",
    fields=("instruction", "context", "response"),
    input_keys=("instruction", "context")
)

parquet_dataset = dl.from_parquet(
    "sample_dataset.parquet",
    fields=("instruction", "context", "response"),
    input_keys=("instruction", "context")
)

pandas_dataset = dl.from_pandas(
    pd.read_csv("sample_dataset.csv"),    # DataFrame
    fields=("instruction", "context", "response"),
    input_keys=("instruction", "context")
)

These are some supported formats that DataLoader supports to load from file directly, in backend most of these methods are leveraging load_dataset method from datasets library to load these formats. But when working with text data you often use HuggingFace datasets, in order to import HF datasets in list of Example format we can use from_huggingface method:

blog_alpaca = dl.from_huggingface(
    "intertwine-expel/expel-blog"
    input_keys=("title",)
)

You can access the dataset of the splits by calling key of the corresponding split:

from  
train_split = blog_alpaca['train']

# Since this is the only split in the dataset we can split this into 
# train and test split ourselves by slicing or sampling 75 rows from the train
# split for testing.
testset = train_split[:75]
trainset = train_split[75:]

The way you load a huggingface dataset using load_dataset is exactly how you load data it via from_huggingface as well. This includes passing specific splits, subsplits, read instructions, etc. For code snippets you can refer to the cheatsheet snippets for loading from HF.